code: def loguniform(low, high, random_state):
      """
      low: an float that represent an lower bound
      high: an float that represent an upper bound
      random_state: an object of numpy.random.RandomState
      """
      assert low > 0, "Lower bound must be positive"
      return np.exp(uniform(np.log(low), np.log(high), random_state))
  
file_path: auptimizer/src/aup/compression/torch/parameter_expressions.py
imports: import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: loguniform(low, high, random_state) and returns: np